code: def forward(self, inp):
      inp = self.embeder(inp).unsqueeze(1)
      convs = [F.relu(conv(inp)).squeeze(3) for conv in self.convs]
      pools = [
          F.max_pool1d(conv, conv.size(2)).squeeze(2) for conv in convs
      ]
      out = torch.cat(pools, 1)
      logit = self.fc(out)
      return logit
  
file_path: TextBox/textbox/evaluator/style_evaluator.py
imports: from .abstract_evaluator import AbstractEvaluator
  import torch
  import torch.nn
  import torch.nn.functional
  from math import ceil
  from transformers import BartTokenizerFast
label_desc: Use image node_id 2 for calling the TextCNN obj's underlying member method code with example usage: obj.forward(inp) and returns: logit